Metadata-Version: 2.1
Name: backprop
Version: 0.1.1
Summary: Backprop
Home-page: https://github.com/backprop-ai/backprop
Author: Backprop
Author-email: hello@backprop.co
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/backprop-ai/backprop/issues
Project-URL: Documentation, https://backprop.readthedocs.io/en/latest/
Project-URL: Source Code, https://github.com/backprop-ai/backprop
Description: <h1 align="center">
           <a href="https://backprop.co">
              <img src=".github/header.png" width="300" alt="Backprop"/>
           </a>
        </h1>
        
        <p align="center">
           <a href="https://pypi.org/project/backprop/"><img src="https://img.shields.io/pypi/v/backprop"/></a> <img src="https://img.shields.io/pypi/pyversions/backprop"/> <a href="https://www.apache.org/licenses/LICENSE-2.0"><img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg"/></a>
        </p>
        
        <p align="center">
        Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.
        </p>
        
        <p align="center">
           <img src=".github/example.png" width="600"/>
        </p>
        
        Solve a variety of tasks with pre-trained models or finetune them in one line for your own tasks.
        
        Out of the box tasks you can solve with Backprop:
        
        - Conversational question answering in English
        - Text Classification in 100+ languages
        - Image Classification
        - Text Vectorisation in 50+ languages
        - Image Vectorisation
        - Summarisation in English
        - Emotion detection in English
        - Text Generation
        
        For more specific use cases, you can adapt a task with little data and a single line of code via finetuning.
        
        You can run all tasks and models on your own machine, or in production with our inference [API](https://backprop.co).
        
        Your finetuned models can be deployed in one line of code.
        
        | ⚡ [Getting started](#getting-started)                | Installation, few minute introduction             |
        | :---------------------------------------------------- | :------------------------------------------------ |
        | 💡 [Examples](#examples)                              | Finetuning and usage examples                     |
        | 📙 [Docs](https://backprop.readthedocs.io/en/latest/) | In-depth documentation about our tasks and models |
        
        ## Getting started
        
        ### Installation
        
        Install Backprop via PyPi:
        
        ```bash
        pip install backprop
        ```
        
        ### Basic task solving
        
        ```python
        from backprop import QA
        
        context = "Take a look at the examples folder to see use cases!"
        
        qa = QA()
        
        # Start building!
        answer = qa("Where can I see what to build?", context)
        
        print(answer)
        # Prints
        "the examples folder"
        ```
        
        See [all available tasks](https://backprop.readthedocs.io/en/latest/Tasks.html).
        
        ### Basic finetuning and uploading
        
        ```python
        from backprop.models import T5
        from backprop import TextGeneration
        
        tg = TextGeneration(T5)
        
        # Any text works as training data
        inp = ["I really liked the service I received!", "Meh, it was not impressive."]
        out = ["positive", "negative"]
        
        # Finetune with a single line of code
        tg.finetune({"input_text": inp, "output_text": out})
        
        # Use your trained model
        prediction = tg("I enjoyed it!")
        
        print(prediction)
        # Prints
        "positive"
        
        # Upload to Backprop for production ready inference
        # Describe your model
        name = "t5-sentiment"
        description = "Predicts positive and negative sentiment"
        
        tg.upload(name=name, description=description, api_key="abc")
        ```
        
        See [finetuning for other tasks](https://backprop.readthedocs.io/en/latest/Finetuning.html).
        
        ## Why Backprop?
        
        1. No experience needed
        
           - Entrance to practical AI should be simple
           - Get state-of-the-art performance in your task without being an expert
        
        2. Data is a bottleneck
        
           - Use AI without needing access to "big data"
           - With transfer learning, even a small amount of data can adapt a task to your niche requirements
        
        3. There are an overwhelming amount of models
        
           - We implement the best open-source and make them simple to use
           - A few general models can accomplish more with less optimisation
        
        4. Deploying models cost effectively is hard work
           - If our models suit your use case, no deployment is needed: just call our API
           - Adapt and deploy your own model with just a few lines of code
           - Our API scales, is always available, and you only pay for usage
        
        ## Examples
        
        - Getting started with Finetuning ([Github](https://github.com/backprop-ai/backprop/blob/main/examples/Finetuning_GettingStarted.ipynb), [Colab](https://colab.research.google.com/github/backprop-ai/backprop/blob/main/examples/Finetuning_GettingStarted.ipynb))
        - [Finetuning and task examples](https://github.com/backprop-ai/backprop/tree/main/examples)
        
        ## Documentation
        
        Check out our [docs](https://backprop.readthedocs.io/en/latest/).
        
        ## Demos
        
        Zero-shot image classification with [CLIP](https://clip.backprop.co).
        
        ## Credits
        
        Backprop relies on many great libraries to work, most notably:
        
        * [PyTorch](https://github.com/pytorch/pytorch)
        * [PyTorch Lightning](https://github.com/PyTorchLightning/pytorch-lightning)
        * [Transformers](https://github.com/huggingface/transformers)
        * [Sentence Transformers](https://github.com/UKPLab/sentence-transformers)
        * [EfficientNet PyTorch](https://github.com/lukemelas/EfficientNet-PyTorch)
        * [CLIP](https://github.com/openai/CLIP)
        
        ## Feedback
        
        Found a bug or have ideas for new tasks and models? Open an [issue](https://github.com/backprop-ai/backprop/issues).
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Requires-Python: >=3.6
Description-Content-Type: text/markdown
